Neural Network projects for M.E., M.Tech, Masters, MS abroad, and PhD students. These Neural Network projects are designed for final year project submissions, research work, and publishing research papers. These research projects guide students to learn, practice, and complete their academic submissions successfully. Each project includes complete source code, project report, PPT, a tutorial, documentation, and a research paper.

Latest Neural Network Projects

  1. A Novel Web Framework for Cervical Cancer Detection System – A Machine Learning Breakthrough
    The main objective of this project is to enable early and accurate detection of cervical cancer using a web-based expert system. It aims to analyze patient data with multiple machine learning algorithms to identify cancer cases reliably. The system focuses on improving diagnosis accuracy, sensitivity, and specificity. It seeks to highlight the most effective algorithms for detection and provide a practical tool to support healthcare professionals. Overall, the project strives to reduce mortality from cervical cancer and enhance women’s healthcare globally.
  2. A Deep Learning Approach to Estimate Multi-Level Mental Stress From EEG Using Serious Games
    This project studies how to measure stress in people while they play a game. It uses a device called EEG to read brain signals and a deep learning model to understand them. The game changes in difficulty, and the system predicts how stressed the player is. The results show that this method can detect stress levels very accurately.
  3. A Novel Three-Dimensional Direction-of-Arrival Estimation Approach Using a Deep Convolutional Neural Network
    This project focuses on finding the direction from which signals come in a three-dimensional space. It uses a special type of artificial intelligence called a deep convolutional neural network to analyze data from a small antenna array. The system can accurately predict angles from any direction, even when multiple signals arrive at the same time. The goal is to make signal detection faster, precise, and reliable in different conditions.
  4. Accelerating Neural ODEs Using Model Order Reduction
    This project makes neural networks faster and more efficient by using ideas from physics and mathematics. It focuses on Neural ODEs, which normally take a long time to run. The researchers use a special method called model order reduction to simplify the calculations without losing accuracy. Their approach works well for tasks like image and time-series classification, making these networks usable on devices with limited resources.
  5. Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges
    This project studies how smart transportation systems can be improved using deep learning. It looks at how traffic, vehicles, roads, and weather affect transportation. The study explains modern AI methods for predicting traffic, recognizing vehicles, and monitoring road conditions. It also highlights challenges and future ideas to make transportation safer and more efficient.
  6. An Epileptic EEG Detection Method Based on Data Augmentation and Lightweight Neural Network
    This project focuses on detecting epilepsy using brain signals. The researchers combined two datasets and improved them to train a small, efficient neural network. The model can accurately identify epilepsy with high reliability. It works well even on low-cost devices and wearable technology, making real-time detection possible.
  7. An Introduction to Adversarially Robust Deep Learning
    This project studies how deep learning models, which are used in fields like medical imaging and speech recognition, can be easily fooled by tiny changes in input data. It reviews past research on making these models more reliable. The work explains why previous solutions did not fully succeed. It also points out new directions for improving the safety and robustness of these systems.
  8. Analysis of a Deep Learning Model for 12-Lead ECG Classification Reveals Learned Features Similar to Diagnostic Criteria
    This project studies how deep learning can detect heart problems from ECG signals. The researchers used a pre-trained neural network and applied explainable methods to see what the model learned. They analyzed which parts of the heart signals influenced the predictions the most. The results show that the model learned features similar to what doctors use in practice.
  9. Attention Feature Fusion Network via Knowledge Propagation for Automated Respiratory Sound Classification
    This project focuses on detecting lung problems in children using recorded breathing sounds. It uses a computer program that learns patterns in these sounds to tell if a child has a respiratory disease. The system works remotely, so doctors can diagnose patients without face-to-face visits. It is more accurate than older methods and can help in situations like pandemics.
  10. Bad and Good Errors: Value-Weighted Skill Scores in Deep Ensemble Learning
    This project focuses on checking how useful predictions are, not just how accurate they are. It gives more importance to predictions that matter most in real situations. The method looks at different types of mistakes and weighs them by their impact. It tests this approach on predictions for pollution, space weather, stock prices, and IoT data, showing better results overall.
  11. BucketAugment: Reinforced Domain Generalisation in Abdominal CT Segmentation
    This project focuses on improving how computers identify organs in CT scans, like kidneys and livers. It introduces a method called BucketAugment that helps neural networks work well on new data from different hospitals. The method uses a smart learning process to find the best way to adjust images during training. Overall, it makes medical image analysis more reliable and flexible across different datasets.
  12. CLADSI: Deep Continual Learning for Alzheimer’s Disease Stage Identification Using Accelerometer Data
    This project uses motion sensors to monitor the movements of Alzheimer's patients. A smart computer model, called a CNN, studies this data to detect the stage of the disease. The model can keep learning as new data comes in, without needing all past data. This helps doctors track patients continuously and supports better care.
  13. Deep Conditional Generative Adversarial Networks for Efficient Channel Estimation in AmBC Systems
    This project improves how battery-free devices communicate using signals from the environment. It uses a deep learning method called a conditional GAN to clean and estimate noisy signal data. The approach learns signal patterns better than older methods and makes communication more accurate and reliable.
  14. Deep Ensemble Learning With Pruning for DDoS Attack Detection in IoT Networks
    This project focuses on protecting Internet of Things devices from online attacks that overload networks, known as DDoS attacks. It introduces a system called DEEPShield, which uses advanced machine learning models to detect both strong and weak attacks quickly. The system works efficiently even on small devices with limited memory. It also uses a new dataset to improve accuracy and reduce errors in detecting threats.
  15. Deep Learning for Radio Resource Allocation Under DoS Attack
    This project develops an intelligent system that helps wireless networks stay secure and efficient even under cyberattacks. It uses deep reinforcement learning to manage how sensors send data and save energy while resisting denial-of-service attacks. The system can also detect when attackers change their strategy and quickly adapt to it. This makes the network more reliable and resilient in real-time conditions.
  16. Deep Reinforcement Learning Assisted UAV Path Planning Relying on Cumulative Reward Mode and Region Segmentation
    This project focuses on making drones fly on their own without a human controlling them. The researchers created a smart system that helps drones choose the best path to reach a destination while avoiding obstacles. It divides the area into smaller regions and rewards the drone for safer, shorter routes. This method helps the drone learn faster and avoid getting stuck in bad paths.
  17. Deep Tensor Spectral Clustering Network via Ensemble of Multiple Affinity Tensors
    This project focuses on grouping data more accurately using a method called tensor spectral clustering. The researchers created a new network, TSC-Net, that learns the data patterns in one step. It reduces memory use by only looking at small parts of the data at a time. Tests show that it groups data better than older methods.
  18. Efficient Quantum Image Classification Using Single Qubit Encoding
    This project explores using quantum computing to classify images more efficiently. It develops a new method that uses only a single quantum bit to mimic traditional deep learning techniques. The approach reduces complexity and requires fewer resources than existing methods. Tests on common image datasets show promising accuracy, and the method could be improved further in the future.
  19. FedDCT: Federated Learning of Large Convolutional Neural Networks on Resource-Constrained Devices Using Divide and Collaborative Training
    This project focuses on making advanced machine learning models usable on devices with limited memory, like smartphones or wearable sensors. Instead of having each device train a big model alone, the method splits the model into smaller parts and lets multiple devices train them together. Devices in a group can also learn from each other, which improves the results. The approach reduces memory needs, speeds up training, and works well on both standard and medical datasets.
  20. GAN-Based Evasion Attack in Filtered Multicarrier Waveforms Systems
    This project studies how advanced AI, called GANs, can trick wireless communication systems. It shows that fake signals made by GANs can look almost exactly like real ones. The research tested this on modern multi-carrier signals used in networks. The results show that receivers can be fooled 99.7% of the time, revealing a serious security risk.
Did you like this research project?

To get this research project Guidelines, Training and Code…

Chat with Expert

How We Help You with Neural Network Projects

At UniPhD, we provide complete guidance and support for Neural Network projects for MTech, ME, Master’s, and PhD students. Our team assists you at every stage from topic selection to coding, report writing, and result analysis.

We also help you choose a suitable IEEE base paper and guide you in developing your project using Python-based tools and frameworks such as TensorFlow, Keras, PyTorch, Scikit-learn, OpenCV, Flask, and Streamlit. In addition, we support implementation and simulation through platforms like MATLAB, Simulink, and NS2, depending on project requirements.

Our experts have extensive experience guiding students in computer science, electronics, and electrical domains, ensuring successful completion of academic and research projects.

Neural Network Thesis and Dissertation Writing

UniPhD has a team of experienced academic writers who specialize in Neural Network research and thesis development. We offer fast-track dissertation writing services to help you complete your Neural Network thesis or dissertation smoothly and on time.

Our M.E., M.Tech, Masters, MS abroad, and PhD theses are developed according to individual university guidelines and checked with plagiarism detection tools to ensure originality and quality.

Neural Network Research Paper Publishing Support

UniPhD provides complete support for research paper writing, editing, and proofreading to help you publish your work in reputed journals or conferences. We accept documents in Microsoft Word, RTF, or LaTeX formats and ensure your paper meets publication standards.

Project Synopsis and Presentation Support

We help you prepare your project synopsis, including the problem definition, objectives, and motivation for your dissertation. Our team also provides complete PPT, documentation, and tutorials to make your final presentation successful. You can also download complete project resources, including source code, a project report, a PPT, a tutorial, documentation, and a research paper for your Neural Network final year project.

Neural Network Research Support for PhD Scholars

UniPhD offers advanced Neural Network research projects designed specifically for PhD scholars. We provide end-to-end support for your research design, implementation, experimentation, and publication process.

Each project package includes comprehensive documentation, including the research proposal, complete source code, research guidance, documentation, research paper, and thesis writing support, helping you successfully complete your doctoral research and academic publications.

Related Posts